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Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina.
PLoS One. 2020; 15(7):e0233855.Plos

Abstract

We aimed to identify variables for forecasting seasonal and short-term targets for influenza-like illness (ILI) in South Korea, and other input variables through weekly time-series of the variables. We also aimed to suggest prediction models for ILI activity using a seasonal autoregressive integrated moving average, including exogenous variables (SARIMAX) models. We collected ILI, FluNet surveillance data, Google Trends (GT), weather, and air-pollution data from 2010 to 2019, applying cross-correlation analysis to identify the time lag between the two respective time-series. The relationship between ILI in South Korea and the input variables were evaluated with Linear regression models. To validate selected input variables, the autoregressive moving average, including exogenous variables (ARMAX) models were used to forecast seasonal ILI after 2 and 30 weeks with a three-year window for the training set used in the fixed rolling window analysis. Moreover, a final SARIMAX model was constructed. Influenza A virus activity peaks in South Korea were roughly divided between the 51st and the 7th week, while those of influenza B were divided between the 3rd and 14th week. GT showed the highest correlation coefficient with forecasts from a week ahead, and seasonal influenza outbreak patterns in Argentina showed a high correlation with those 30 weeks ahead in South Korea. The prediction models after 2 and 30 weeks using ARMAX models had R2 values of 0.789 and 0.621, respectively, indicating that reference models using only the previous seasonal ILI could be improved. The currently eligible input variables selected by the cross-correlation analysis helped propose short-term and long-term predictions for ILI in Korea. Our findings indicate that influenza surveillance in Argentina can help predict seasonal ILI patterns after 30 weeks in South Korea, and these can help the Korea Centers for Disease Control and Prevention determine vaccine strategies for the next ILI season.

Authors+Show Affiliations

Department of Data-centric Problem Solving Research, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea. Center for Convergent Research of Emerging Virus Infection, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea.Department of Data-centric Problem Solving Research, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea. Center for Convergent Research of Emerging Virus Infection, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't
Validation Study

Language

eng

PubMed ID

32673312

Citation

Choi, Soo Beom, and Insung Ahn. "Forecasting Seasonal Influenza-like Illness in South Korea After 2 and 30 Weeks Using Google Trends and Influenza Data From Argentina." PloS One, vol. 15, no. 7, 2020, pp. e0233855.
Choi SB, Ahn I. Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina. PLoS ONE. 2020;15(7):e0233855.
Choi, S. B., & Ahn, I. (2020). Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina. PloS One, 15(7), e0233855. https://doi.org/10.1371/journal.pone.0233855
Choi SB, Ahn I. Forecasting Seasonal Influenza-like Illness in South Korea After 2 and 30 Weeks Using Google Trends and Influenza Data From Argentina. PLoS ONE. 2020;15(7):e0233855. PubMed PMID: 32673312.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina. AU - Choi,Soo Beom, AU - Ahn,Insung, Y1 - 2020/07/16/ PY - 2020/03/02/received PY - 2020/05/13/accepted PY - 2020/7/17/entrez PY - 2020/7/17/pubmed PY - 2020/9/9/medline SP - e0233855 EP - e0233855 JF - PloS one JO - PLoS ONE VL - 15 IS - 7 N2 - We aimed to identify variables for forecasting seasonal and short-term targets for influenza-like illness (ILI) in South Korea, and other input variables through weekly time-series of the variables. We also aimed to suggest prediction models for ILI activity using a seasonal autoregressive integrated moving average, including exogenous variables (SARIMAX) models. We collected ILI, FluNet surveillance data, Google Trends (GT), weather, and air-pollution data from 2010 to 2019, applying cross-correlation analysis to identify the time lag between the two respective time-series. The relationship between ILI in South Korea and the input variables were evaluated with Linear regression models. To validate selected input variables, the autoregressive moving average, including exogenous variables (ARMAX) models were used to forecast seasonal ILI after 2 and 30 weeks with a three-year window for the training set used in the fixed rolling window analysis. Moreover, a final SARIMAX model was constructed. Influenza A virus activity peaks in South Korea were roughly divided between the 51st and the 7th week, while those of influenza B were divided between the 3rd and 14th week. GT showed the highest correlation coefficient with forecasts from a week ahead, and seasonal influenza outbreak patterns in Argentina showed a high correlation with those 30 weeks ahead in South Korea. The prediction models after 2 and 30 weeks using ARMAX models had R2 values of 0.789 and 0.621, respectively, indicating that reference models using only the previous seasonal ILI could be improved. The currently eligible input variables selected by the cross-correlation analysis helped propose short-term and long-term predictions for ILI in Korea. Our findings indicate that influenza surveillance in Argentina can help predict seasonal ILI patterns after 30 weeks in South Korea, and these can help the Korea Centers for Disease Control and Prevention determine vaccine strategies for the next ILI season. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/32673312/Forecasting_seasonal_influenza_like_illness_in_South_Korea_after_2_and_30_weeks_using_Google_Trends_and_influenza_data_from_Argentina_ L2 - https://dx.plos.org/10.1371/journal.pone.0233855 DB - PRIME DP - Unbound Medicine ER -